ArticleScientific reports2022
Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Study on mental fatigue detection of crane operators based on frequency and channel attention spiking neural network with multivariate variational mode decomposition in real construction environments.Medical & biological engineering & computing · 2026Article
- Identifying neuromuscular and mental fatigue in elite youth table tennis players using machine learning.Scientific reports · 2026Article
- Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases.Frontiers in digital health · 2026Article
- Advancing entropy analysis for heart rate variability: clinical insights for aging and diabetes.Frontiers in physiology · 2026Review
- Unveiling psychobiological correlates in primary Sjögren's syndrome: a machine learning approach to determinants of disease burden.Frontiers in psychiatry · 2025Article
- Artificial Intelligence-Driven Approaches to Managing Surgeon Fatigue and Improving Performance.Cureus · 2024Article
- Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.BMC public health · 2024Article
- Next Generation Computing and Communication Hub for First Responders in Smart Cities.Sensors (Basel, Switzerland) · 2024Article
- [Mental fatigue state recognition method based on convolution neural network and long short-term memory].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Time-domain heart rate variability features for automatic congestive heart failure prediction.ESC heart failure · 2024Article
- Acute Effects of Fatigue on Cardiac Autonomic Nervous Activity.Journal of sports science & medicine · 2023Article
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4 authors.
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Abstract
A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to detect fatigue in order to prevent its consequences. However, the results of these studies cannot be generalised because of various methodological issues including the use of only one type of cognitive task to induce fatigue which makes any predictions task-specific. In this study, we combined the datasets of three experiments each of which applied different cognitive tasks for fatigue induction and trained algorithms that detect fatigue and predict its severity. We also tested different time window lengths and compared algorithms trained on resting and task related data. We found that classification performance was best when the support vector classifier was trained on task related HRV calculated for a 5-min time window (AUC = 0.843, accuracy = 0.761). For the prediction of fatigue severity, CatBoost regression showed the best performance when trained on 3-min HRV data and self-reported measures (R
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